Generates an array containing a Binomially-distributed pseudorandom pattern of values that are the number of occurrences of an event, given the probability of that event occurring and the number of trials.

Riffles the input array of NIComplexNumber elements by randomly selecting two elements in inputArray, swapping those elements, and then repeating this process numberOfElements times, where numberOfElements is the size of inputArray.

Riffles the input array of integer elements by randomly selecting two elements in inputArray, swapping those elements, and then repeating this process numberOfElements times, where numberOfElements is the size of inputArray.

Generates an array containing a Poisson-distributed pseudorandom pattern of values that are the number of discrete events that occur in the interval specified by the mean of a unit rate Poisson process.

Riffles an input array of double-precision elements by randomly selecting two elements in inputArray, swapping those elements, and then repeating this process numberOfElements times, where numberOfElements is the size of inputArray.

Takes an array of experimental observations you make at various levels of some factor, with at least one observation per factor, and performs a one-way analysis of variance (ANOVA) in the fixed effect model.

Calculates the single-sided coherence function along with the averaged single-sided cross power spectrum, averaged single-sided frequency response, or transfer function, and impulse response, from a 2D array of stimulus signals and a 2D array of response signals.

Uses the Levenberg-Marquardt algorithm to determine the least squares set of coefficients that best fit the set of input data points (x, y) as expressed by a nonlinear function y = f(x, a) where a is the set of coefficients.

Uses the Levenberg-Marquardt algorithm to determine the least squares set of coefficients that best fit the set of input data points (x, y) as expressed by a nonlinear function y = f(x, a) where a is the set of coefficients.

Uses the Levenberg-Marquardt algorithm to determine the least squares set of coefficients that best fit the set of input data points (x, y) as expressed by a nonlinear function y = f(x, a) where a is the set of coefficients.

Calculates the value of the unique polynomial P of degree (numberOfElements - 1) passing through the numberOfElements points (xi, f(xi)) at x_value and returns an estimate of the error in the interpolation, given a set of numberOfElements points (xi, f(xi)) in the plane where f is some function and given a value x_value at which f is interpolated or extrapolated.

Returns the value of a particular rational function P(x)/Q(x) passing through the numberOfElements points (xi, f(xi)) at x_value, given a set of numberOfElements points (xi, f(xi)) in the plane where f is some function, and a value x_value at which f is to be interpolated.

Performs a cubic spline interpolation of the function f at a value x_value, where x_value is in the same range as xi, given a tabulated function of the form yi = f(xi) for i = 0, 1, . . ., numberOfElements - 1, with x < xi + 1, and given the second derivatives that specify the interpolant at the numberOfElements nodes of arrayX.

Calculates the second derivatives used by the cubic spline interpolant, given a tabulated function of the form yi = f(xi) for i = 0, 1, . . ., numberOfElements - 1, with xi < xi + 1, and given the boundary conditions firstBoundary and secondBoundary such that the interpolant's second derivative matches the specified values at x0 and xn - 1.

Designs a digital bandstop FIR linear phase filter using a windowing technique. Five windows are available. Wind_BSF generates only the filter coefficients; it does not actually perform data filtering.